Generation of Natural Language Notifications

ABSTRACT

Natural language notification generation techniques and system are described. In an implementation, natural language notifications are generated to provide insight into alerts related to a metric, underlying causes of the alert from other metrics, and relationships of the metric to other metrics. In this way, a user may gain this insight in an efficient, intuitive, and time effective manner.

BACKGROUND

Marketers are tasked with increasing the likelihood that a user willinteract with digital marketing content and/or other content related tothe purchase of a product or service. This is also referred to asconversion.

In order to do so, Marketers first process large volumes of analyticsdata using computing devices to understand previous consumption patternsand strategize how to increase conversions. This understanding, due tothe large volumes of the analytics data, is not possible by theMarketers without use of the computing devices. Analytics data describespast user interaction with a service provider as part of conversion of aproduct or service. Through processing of this analytics data by thecomputing devices, the Marketer may select digital marketing contentthat has exhibited increases in conversion of the product or service.The amount of analytics data that is provided to marketers, however, isever increasing. Further, marketers typically have a limited amount oftime to arrive at actionable insights regarding this data obtainedthrough the processing by the computing devices, e.g., in order totimely provide accurate digital marketing content based on currentconditions. Accordingly, an amount of time taken to processing theanalytics data by the computing devices may adversely affect theMarketer's ability to gain and take action on this insights.

Conventional techniques used to expose this data, however, are difficultto parse by marketers, especially in the limited amount of timeavailable. For example, although tables and numbers may provide vastamounts of information, significance of these tables and numbers may notbe readily evident to the marketers. Further, rapidly changingconditions may limit usefulness of these insights. Accordingly, the everincreasing amounts of data that are made available to the marketers may,in effect, limit the usefulness of this data by an inability to gaininsight in a timely manner into what is represented by the data throughprocessing by the computing devices using conventional techniques.

SUMMARY

Natural language notification generation techniques and systems aredescribed. In an implementation, natural language notifications aregenerated to provide insight into alerts related to a metric (e.g., aninsight into a change observed in the user interaction), underlyingcauses of the alert from other metrics, and relationships of the metricto other metrics. In this way, a user may gain this insight in anefficient, intuitive, and time effective manner.

This Summary introduces a selection of concepts in a simplified formthat are further described below in the Detailed Description. As such,this Summary is not intended to identify essential features of theclaimed subject matter, nor is it intended to be used as an aid indetermining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description is described with reference to the accompanyingfigures.

FIG. 1 is an illustration of an environment in an example implementationthat is operable to employ natural language notification techniquesdescribed herein.

FIG. 2 depicts a system showing operation of a notification system ofFIG. 1 in greater detail.

FIG. 3 depicts an example implementation of a natural languagenotification as output in a user interface.

FIG. 4 is a flow diagram depicting a procedure in an exampleimplementation in which natural language notifications are generatedbased on user interaction with a service provider.

FIG. 5 is a flow diagram depicting a procedure in an exampleimplementation in which natural language notifications are generatedbased on an alert.

FIG. 6 illustrates an example system including various components of anexample device that can be implemented as any type of computing deviceas described and/or utilize with reference to FIGS. 1-5 to implementembodiments of the techniques described herein.

DETAILED DESCRIPTION

Overview

Conventional techniques used to communicate analytics and other data tousers are typically difficult to parse. For example, tables, rawnumbers, and so forth may provide a vast amount of information, but thisinformation may not be readily understood by a user. Even if understood,it may be interpreted wrongly if the user is not familiar with what isthe information about, or how it was calculated. This is furthercomplicated by situations in which the data may become stale, e.g., dueto ever-changing conditions.

Natural language notification generation techniques and systems aredescribed. In the following, these techniques are described in relationto a marketing environment. However, it should be readily apparent thatthese techniques are equally applicable to a variety of otherenvironments in order to abstract data changes and relationshipsdescribed by data through use of a natural language notification.

In one example, natural language notifications are employed to fill agap in understanding of conventional tables and numbers to enable amarketer to readily gain insight into what is represented by thesenumbers. This enables marketers to address sudden or significant changesby presenting this information in a form that is easily consumable andrelevant.

In order to generate the natural language notifications, data is firstobtained that describes user interaction with a service provider, e.g.,a website that makes goods or services available for purchase. The datais then processed to locate metrics that cause generation of anotification, e.g., through comparison of values of the metrics withdefined thresholds. Metrics include data insights, such as those relatedto changes in dimension data and other quantities observable from thedata. These metrics, as well as related metrics, are then used togenerate statements (e.g., “facts”) describing a relationship of themetrics with each other as well as different aspects of the metrics. Forexample, for a change in conversion rate over a threshold amount,statements are generated that describe the amount of change, a referencepoint for the change, possible causes of this change, factors affecting,correlated quantities, and any other information that may be conveyedbased on the supplied information. In this way, the different aspects ofthe metric “conversion rate” as well as relationship to other metricsthat may be indicative of a cause of the metric are used to generatethese statements.

The statements are then used to form one or more natural languagephrases. The statements, for instance, may be used to select from a setof statement templates that include the statements. Continuing with theprevious example, a statement template “Sales are up/down by ______since______” is selected based on an aspect of the conversion ratemetric. This statement template is then filled in with the appropriatemetrics and rectified to generate the natural language phrase, e.g.,“sales are up by 23% since last year.” This process may continue for aplurality of the different aspects of the metric to select a pluralityof different statement templates and thus generate a plurality ofnatural language phrases. Additionally, the process may be configuredfor multi-lingual support, e.g., through use of templates configured fordifferent languages.

The natural language notification is then output that includes one ormore of these natural language phrases. For example, the naturallanguage phrases may be arranged in the notification based on aweighting, e.g., larger or more descriptive phrases are orderedaccordingly within the notification. Additionally, user preferences mayalso be employed to arrange text, order, layout, or dispatch of thenatural language notification, such as based on a user's role, profile,or usage information. In this way, the natural language notificationsare generated to enable marketers and other users to gain insightsusable to control provision of digital marketing content. Furtherdiscussion of these and other examples is included in the followingsections.

In the following discussion, an example environment is first describedthat may employ the techniques described herein. Example procedures arethen described which may be performed in the example environment as wellas other environments. Consequently, performance of the exampleprocedures is not limited to the example environment and the exampleenvironment is not limited to performance of the example procedures.

Example Environment

FIG. 1 is an illustration of an environment 100 in an exampleimplementation that is operable to employ natural language notificationtechniques described herein. The illustrated environment 100 includes aservice provider 102, an analytics service 104, a marketing service 106,and a client device 108 that are communicatively coupled, one toanother, via a network 110. Computing devices that are usable toimplement the service provider 102, analytics service 104, marketingservice 106, and client device 108 may be configured in a variety ofways.

A computing device, for instance, may be configured as a desktopcomputer, a laptop computer, a mobile device (e.g., assuming a handheldconfiguration such as a tablet or mobile phone as illustrated), and soforth. Thus, the computing device may range from full resource deviceswith substantial memory and processor resources (e.g., personalcomputers, game consoles) to a low-resource device with limited memoryand/or processing resources (e.g., mobile devices). Additionally, acomputing device may be representative of a plurality of differentdevices, such as multiple servers utilized by a business to performoperations “over the cloud” as further described in relation to FIG. 6.

The service provider 102 is illustrated as implemented at leastpartially in hardware (e.g., through the use of one or more modules) toprovide services accessible via the network 110 that are usable to makeproducts or services available to a user population 112. The serviceprovider 102, for instance, may expose a website or other functionalitythat is accessible via the network 110 by computing devices of the userpopulation 112, e.g., mobile phones, desktop computers, game consoles,and so forth. In another example, the service provider 102 transmitscommunications (e.g., emails) to the user population that promote theproduct or service of the service provider 102.

The analytics service 104 collects service data 114 through use of ananalytics manager module 116 that is implemented at least partially inhardware. The service data 114 may describe a variety of informationrelating to the service provider 104. In one example the service data114 describes interaction of the user population 112 with the servicesof the service provider 102. This may include navigation of usersthrough particular webpages of the service provider 102, types ofhardware of software used by the user population to access the services,and so on. The service data 114 may also include data that describesoperation of the service provider 102, itself. This may includehardware, software, and network functionality of the service provider102. A variety of other examples are also contemplated, such asinteraction of the user population 112 with other service providers.

The analytics service 104 then communicates metric data 118 to amarketing manager module 120 of the marketing service 106. The marketingmanager module 120, for instance, may request metric data 118 thatdescribes particular metrics included in the service data 114 collectedby the analytics service 104 that are of interest to a marketer. Themetric data 118 is then processed by a notification system 122 that isimplemented at least partially in hardware to generate a naturallanguage notification 124.

The natural language notification 124 is configured to conveyinformation relating to metrics collected as part of the metric data118. This may include notification of changes to operation of theservice provider 102, user interactions, alerts generated for detecteddeviations in values of metrics over a defined threshold, and so forth.The natural language notification 124 is communicated via the network110 to a client device 108 for output by a communication module 126within a user interface 128, e.g., as an email, instant message, displaywithin a browser or other network-enabled application, and so on.

As previously described, marketers are typically confronted with largeamounts of data in a limited time to arrive at actionable insights.Examples of such insights are sudden or significant changes in webmetrics at present, predictions of changes in future, and so forth.Accordingly, in the following the natural language notification 124 isconfigured to capture and to generate information in a form that iseasily consumable and relevant to the marketer. This may include asummary of the data changes in the metric data 118 at a high level,which may also highlight the key noticeable trends and relationships. Asillustrated for the displayed natural language notification 124 in theuser interface 128, a high level of “Average Order Value encountered aSevere Change in the Previous Day” gives a user of the client device 108high level insight to a purpose of the notification. The naturallanguage notification 124 also includes a context to this insight inbulleted points below and may even include possible causes as furtherdescribed in relation to FIG. 2.

The notification system 112 may also incorporate user preferences inselection, layout and detail-orientation of the natural languagenotification 124. For example, not all information may be relevant forevery user. For example, an analytically-trained user may desiredetailed information, broken down into its various contributingdimensions. However, a user trained in business may desire a cursoryalerting of some numbers, with more focus on a different set of metrics.Thus, different users may have different objectives regarding what isincluded in the natural language notification 124. This may includedifferent set of metrics to be alerted, or different levels of detail interms of the granularity of the time series (e.g., hourly or daily) orin terms of other data attributes (e.g., different levels of granularityfor geographic locations) for a single set of metrics. Furthermore,notifications may be configured for different languages or differentvocabularies, depending on the location or the industry for which thisalert is prepared.

Additionally, depending on the desired style of quantitative analysis ofthe user, textual or visual descriptions may be employed as part of thenatural language notification 124. In this way, the natural languagenotifications 124 described in the following address the actionable,functional needs and the personal preferences of the users. Thisfunctions to fine tune a level of detail to share about a metric and itstrends, as well as its relationship with other metrics being tracked.Further discussion of generation of the natural language notification124 by the notification system 122 is described in the following.

FIG. 2 depicts a system 200 showing operation of the notification system122 of FIG. 1 in greater detail. A change in one metric is often relatedto another. Conventionally, the onus is on the user in order to identifythis relationship. The notification system 122 described herein,however, is configured to determine and express relationships betweenmetrics as a meaningful natural language description. In the following,the natural language notification 124 is described in relation to amarketing environment and metrics refer to website metrics. However, itshould be readily apparent that the techniques described herein areequally applicable to a wide range of time varying metrics, such asserver usage logs, hardware performance logs, and so forth.

To begin, the notification system 122 includes a metric collectionmodule 202 that is implemented at least partially in hardware to collectmetric data 118 for metrics 204. Metrics 204 may define any measurablecharacteristic, e.g., page views, network bandwidth, processor load,amount of memory storage, navigation pattern through digital content,and so forth. The metrics 204 are used as a basis to form the naturallanguage notification 124. The metric data 118, for instance, may beconfigured as time series and relationship information 206 for themetrics 204 reported by the analytics service 104 of FIG. 1. The metricdata 118 may also include data related to the metrics obtained fromsources other than the service provider 102, such as from relatedservice providers, network 110 functionality, and so forth.

Relationships between metrics may be pre-defined or dynamically derivedby the metric collection module 202, e.g., through semantic analysis. Ina pre-defined example, different types of tags are assignedautomatically to each metric. From this, relationships between metricsmay be readily determined based on these tags. For instance, semantictags may be used to describe a type of user interaction with the serviceprovider 102 for a metric. Types of user interactions include “searchclick past event,” “search click through,” “search click,” and so forth.

In another instance, functional tags are used that describe a type ofinformation being tracked by the metric and thus a relationship betweenthe data associated with a functional tag as well as data associatedwith other functional tags. Examples of functional tag descriptioninclude “e-commerce traffic,” “screen rendering,” “geographic location,”and so forth. In a further instance, customer journey tags are used todescribe a journey through digital content by a user. Examples ofjourney tags include description of a stage in a conversion scenario,browsing of particular items of digital content of the service provider102 (e.g., webpages), purchases from the service provider 102, and soforth. In yet another instance, persona tags are used to describe a typeof user that is typically interested in a respective metric. Examples ofpersona tags include business roles or other demographic information. Avariety of other tag types are also contemplated, such as resource tagsdescribing resources used as part of consumption that is described by ametric, such as hardware, software, or network resources.

From this metric data 118, a statement generation module 208 isimplemented at least partially in hardware to generate statements 210regarding the metrics 204. The statements 210 act as meta-information tohighlight trends exhibited by the metrics 204. Examples of statementsinclude historical value of a metric 204 at different reference points(e.g., in time) for comparison purposes, anomaly history andcontribution analysis, relationships between metrics 204 based onpre-specified information or semantic similarities between metric namesor descriptions, and so forth.

Thus, statements 210 are the pieces of meta-information used to createan explanation for different aspects of a metric 204, and otherinformation related to the metric 214, such as the number of times ithas had an anomalous value, and so on. An example of a list ofstatements 210 to be generated for a single metric is included in thefollowing table.

Example statement Statement template Comments Total “A totalof_anomalies Provided from anomaly detection anomalies were detected”source, e.g., the service provider 102, analytics service 104, ormarketing service 106 Related 1. “Possibly related Provided from thesource which anomalies anomalies:” provides key anomalies 2. “Theanomalies in related metrics are” Percent 3. “up 16% as Based on storedvalues of metrics change/ compared to the for a fixed amount of time forSeverity average/last week” comparison of values. 4. “large increase”,“small dip” Reference 1. “as compared to Used to decide what referencepoint/ last April/last point to consider for comparison multipleweek/last of a metric's values. reference year/average” points 2. Lastyear/week/month, this value was . . . 3. Graph Look-up “This metriccalculates Provides a full description of the table for the . . . ”metric for increased description understanding. Contribution “Majorcontributors to To provide contribution analysis analysis this anomalyare . . . ” Causal “This anomaly is To provide causes for change inanalysis driven by . . . ” the metric Anomaly “This anomaly last To giveinformation regarding history occurred . . . ” how many times and whenan “This anomaly anomaly occurred for the metric. maintained itsposition . . . ” Suggested “We also found the To suggest some metricswhich anomalies following anomalies the user might want to track. whichyou have not been tracking, but they are severe anomalies: . . . ”.Explanation “This is based on your The factors which triggered the ofinterest in . . . ” suggestion suggestions

A variety of techniques may be employed to obtain values for each of thestatements 210. Examples of such techniques include processing by thestatement generation module 208, an API call to another service provideror database, and so forth. For each metric 204, the statement generationmodule 208 tests which statements 210 can be created and whatinformation is available before generating a set of possible statements,referred to as “Sp” in the following.

The statement generation module 208 then selects a sub-set of statements210 from a set of possible statements 210, e.g., to explain an anomaly.Selection may be based on a variety of criteria. Examples of suchcriteria include permissible length of the natural language phrases 214or natural language notification 124, permissible amount of informationfor a metric, user persona for whom the report is intended and usercustomization preferences, and so forth. This results in the selectedset of statements, “Ss”.

A phrase generation module 212 is implemented at least partially inhardware to accept the selected set of statements 210 as inputs. Fromthese inputs, the phrase generation module 212 generates naturallanguage phrases 214. The phrase generation module 212, for instance,may be configured to select appropriate statement templates based on thereceived statements 210. The statement templates are then completedusing the statements 210 and rectified to form natural language phrases214. For example, the statements 210 “20%” for a metric “conversionrate” that has increased are inserted into a statement template of “Theconversion rate has shown a ______ increase/decrease.” The filled instatement template is then rectified by the phrase generation module 212to follow proper language forms, e.g., “The conversion rate has shown a20% increase.”

For example, the phrase generation module 212 may employ heuristics totake the selected set of statements “Ss” as an input and process it tooutput a set of appropriate statement templates. This is performed bydetecting the presence of various statements 210 in “Ss”, which is thenused as a basis to select applicable statement templates. The phrasegeneration module 212, for instance, may employs rules in the form ofthe following: “if values of facts {A1, A2, . . . , An} are present,then choose template Tk.”

The rules have an assigned priority order, i.e., a hierarchicalrelationship. If a rule is satisfied, then, to reduce duplication ofinformation, a subset of rules is removed from further checks and onlythe remaining rules are checked for selecting further templates. On suchexample is described as follows:

-   -   Check if values are present for following statements: key        anomaly A, related anomaly B, number of times anomaly A occurred        in last month, number of times related anomaly B occurred in        last month; and    -   If the values for these statements are present, the statement        template selected is “An anomaly was detected on Metric A, $        times in last month, while its related metric had an anomaly $        times.”        The statements templates thus selected, along with the        statements 210 and the values for the statements 210, are used        to create the natural language phrases 214 as follows.

The phrase generation module 212 then inserts values of the statements210 within the statement templates at appropriate slots. For example,slots are positions within the templates marked as using “$” in thefollowing.

1. Overall Alert Suite Description

-   -   a. Of the total $ metrics, anomalies were found in $ metrics        since $.    -   b. A total of $ metrics were analyzed and anomalies were        detected in $ metrics.

2. Metric Description Examples

-   -   a. Hits—Total number of hits sent to the analytics service 104.        This metric sums hit types (e.g., page views, event, timing, and        so forth).    -   b. sessionDuration—The length of a session measured in seconds        and alerted in second increments.

3. Comparisons/Reference

-   -   a. $ is up/down $% since $.    -   b. $ is continues to climb/grow/rise over $.    -   c. The values of $ has increased/decreased/changed $%        -   i. Traffic from Affiliate continues to climb/grow/rise over            week, and is up from April.        -   ii. Revenue: $264,087 (up 16%, significant compared to            historical averages). iii. Revenue is up 20% compared to            May 2013. It's also the highest revenue has ever been at            this point in any 2nd Quarter.

4. Anomaly History

-   -   a. The metric hits encountered (another anomaly)/$ anomalies in        the last $(time period).    -   b. The metric $ encounters anomaly every $.    -   c. We have found $ anomalies in the metric since you started        tracking it.

5. Contribution

-   -   a. Major contributors to this anomaly are $    -   b. $ are the major contributors to $.    -   c. $ is driven by $.

6. Overall Review of Important Metrics

-   -   a. Increases in $ observed in the past $.

As part of filling the statement template, the phrase generation module212 makes lexical choices based on conditions in order to rectify thestatement 210 into a proper language form. For example, in the followingtemplate “The value of the metric $1 changed from $2 to $3,” for $1 thephrase generation module 212 uses “increased” and “decreased” otherwise.Other forms of lexical choices include use of different phrases orwords, e.g., using “went up” instead of “increased”. Other kind ofconsiderations undertaken by the phrase generation module 212 includedeciding the form of word, its tense, singular vs plural and othermorphological considerations, spelling, capitalization, breaks betweenwords, punctuation, and other orthographical considerations.

The natural language phrases 214 are then processed by a phraseaggregation module 216 that is implemented at least partially inhardware to form the natural language notification 124 from the naturallanguage phrases 214. The phrase aggregation module 216, for instance,may select from the natural language phrases 214 to arrive at a set ofphrases that best represent an underlying purpose of the notification aswell as remove repetition.

For example, phrases may express a same concept to different degrees andthus the natural language phrase 214 that expresses this concept to thelesser degree may be removed from consideration as part of the naturallanguage notification 124. A first natural language phrase, forinstance, may indicate that “The conversion rate has shown a 20%increase” as previously described. A second natural language phrase, onthe other hand, may indicate that “The conversion rate has shown a 20%increase over the past quarter since introduction of the buy one/get onefree marketing campaign.” Accordingly, the second natural languagephrase 214 is chosen for inclusion in this example whereas the firstnatural language phrase is not.

The phrase aggregation module 216 may also employ functionality toreduce repetition in the natural language phrases 214. An example ofthis is use of co-reference resolution using pronouns. Therefore,instead of using an actual name of the metric 204, “the metric” or “it”may be used as part of the natural language phrase. In another example,the natural language phrase 214 generated for the natural languagenotification 124 may be stored in a temporary storage buffer whilenotification is being generated. In this way, repetition of lexicalchoices may be avoided such that different words and phrases are chosento describe successive anomalies.

The phrase aggregation module 216 may also be configured to personalizethe natural language notification 124 based on one or more userpreferences. The phrase aggregation module 216, for instance, may bemade aware of a potential viewer of the natural language notification124 and select natural language phrases 214 consistent with userpreferences of that viewer. User preferences may be based on a user'srole, profile, or usage information that is to receive the naturallanguage notification. As previously described, different users maydesire different levels of detail, use of different metrics to describean underlying condition, and so on. Accordingly, configuration of thenatural language notification 124 based on these considerationsincreases usefulness of the notification to a wider range of users.Similar functionality may also be incorporated as part of the phrasegeneration module 212 to generate phrases based on user preferences.

The natural language notification 124 is then output by the notificationsystem 122 for display in a user interface. As part of this, the naturallanguage notification 124 may include an embedded module configured tocollect feedback from recipients of the notification. This feedback maybe employed in a variety of ways, such as to change a weightingregarding how natural language phrases 214 are chosen for inclusion inthe natural language notification 124 by the phrase aggregation module216.

FIG. 3 depicts an example implementation 300 of a natural languagenotification 124. The natural language notification in this example ispersonalized based on user preferences. As illustrated, the notificationis addressed to a particular user 302 and has a level of detail andlanguage usage based on the user's role, profile, or usage information.

The natural language notification 124 also indicates a respective metric304 that is a subject of the notification and includes informationregarding different aspects of that metric. As illustrated, the metric“average order value” is illustrated along with aspects of the metric,such as how the metric is formed, values of the metric, productsrelating to the metric, and so forth.

The natural language notification 124 also includes indications ofpotential causes 306 of an alert that caused formation of thenotification. For example, the illustrated natural language notification124 is generated responsive to comparison of values of a metric “averageorder value” with a threshold. The notification system 122 alsodetermines potential contributing causes 306 of this condition, examplesof which include average unit retail went up 19%, units per order wentup 11%, and order per visit went up 5%. The contributing causes 306 maybe determined in a variety of ways, such as based on variation in trendsexhibited by these metrics over time, semantic relationships, and soforth.

Accordingly, the natural language notification 124 provides datadescriptions based on the indexed descriptions of the metrics 204 beingtracked by the user. Given a set of pre-selected metrics 204 and values,which are to be included in the natural language notification 124, thesetechniques provide a way to construct a message that explains this datain plain language, along with the pre-specified (or automaticallyidentified) relationships between the chosen metrics. These techniquesare further usable to generate a human consumable message which providesa set of statements 210 about the metrics 204, meta-information,observed trends or fluctuations in their observations, and observedrelationships with other metrics and dimensions. The techniques may beused to calculate metric relationships between metrics, which may bepre-calculated or identified based on semantic similarities betweenmetric descriptions.

Additionally, contextual information is provided to link the metricswith the trends in data over a different period of time. This may beused to link trends in news and trends as compared to popular metrics,filters and granularities which have been typically used for reporting,and so forth. Further, explanations may be tendered for informationincluded in the notification, such as if the user is receiving anotification based on a metric regarding prior usage, preferences innotifications, or even based on the usage and/or preferences of othersimilar users. Complementary information in the form of suggestedactions, relevant links and images/graphs may be provided for betterunderstanding of the alerted metric. Further, as described abovecustomization of the text, arrangement, order, layout and dispatch ofthe notification may be employed. As such, the natural languagenotification 124 is customized according to the preferential order,device usage, browsing behavior and time preferences, implicitly orexplicitly specified by the user. Cognitive loading concepts may also beapplied by the phrase aggregation module 216 to manage how text andgraphs complement each other in an alert message. For example, anomalouschanges in data may be depicted graphically in a meaningful way thatrelates to the textual description.

Example Procedures

The following discussion describes techniques that may be implementedutilizing the previously described systems and devices. Aspects of eachof the procedures may be implemented in hardware, firmware, or software,or a combination thereof. The procedures are shown as a set of blocksthat specify operations performed by one or more devices and are notnecessarily limited to the orders shown for performing the operations bythe respective blocks. In portions of the following discussion,reference will be made to FIGS. 1-3.

FIG. 4 depicts a procedure 400 in an example implementation in whichnatural language notifications are generated. Data is obtaineddescribing metrics involving user interaction via a network with theservice provider (block 402). The data, for instance, may include metricdata 118 obtained from an analytics services, directly from a web siteof a service provider 102, and so forth.

Statements are generated describing a plurality of different aspects ofrespective said metrics and relationships of the metrics to each other(block 404). The statements 210, for instance, may be generated by thestatement generation module 208 to describe trends, alerts, and so forthexhibited by the data.

At least one natural language phrase is formed based on the generatedstatements by rectifying a set of statement templates as includingrespective said statements (block 406). Statements 210 of “conversionrate,” “increase” and “15”, for instance, may be inserted into astatement template having “blanks” that correspond to those statements.The filled-in statement templates are then rectified into properlanguage forms, redundancies are removed, and so forth.

A natural language notification is output that includes the at least onenatural language phrase (block 408). The phrase aggregation module 216,for instance, may generate the natural language notification 124 byselecting form the natural language phrases 214. This may also includefurther rectification of these phrases, such as to remove redundanciesbetween phrases. Supplementary information may also be included, such asgraphs, charts, and so on that are generated automatically based onvalues of the metrics 204, which may provide further insight to a user.

FIG. 5 depicts another procedure 500 in an example implementation inwhich natural language notifications are generated based on userinteraction with a service provider. Data is obtained describing analert involving a comparison of a metric of the service provider with athreshold (block 502). For example, a determination may be made that oneor more metrics 204 have values that depart from a baseline by more thana threshold amount. This may include instances of increases of sales,drops in network traffic, and so forth.

Statements are generated describing at least one other metric thatcaused the alert of the metric (block 504). The at least one othermetric may be identified in a variety of ways. In one example, the othermetric is based on tags (block 506), such as through use of the semanticfunctional, customer journey, or persona tags previously described. Inanother example, the other metric is based on a semantic analysis (block508), such as to determine related metrics to the metric that involvesthe alert. In a further example, the other metric is identified based ona threshold comparison (block 510), such as another metric that whilenot exceeding a threshold has approached the threshold. In anotherexample, a threshold of a related metric is adjusted based on the alert.For example, the average order value metric of FIG. 3 may exceed athreshold. Accordingly, thresholds used for related metrics such asaverage unit retail, units per order, order per visit and other may belowered in order to detect changes to these other metrics. A variety ofother examples are also contemplated.

At least one natural language phrase is formed based on the generatedstatements by rectifying a set of statement templates as includingrespective said statements (block 512). A natural language notificationis output that includes the at least one natural language phrase (block514). The selection of statement templates, rectification, and selectionof phrases for inclusion as part of the notification may proceed asdescribed above.

Example System and Device

FIG. 6 illustrates an example system generally at 600 that includes anexample computing device 602 that is representative of one or morecomputing systems and/or devices that may implement the varioustechniques described herein. This is illustrated through inclusion ofthe notification system 122. The computing device 602 may be, forexample, a server of a service provider, a device associated with aclient (e.g., a client device), an on-chip system, and/or any othersuitable computing device or computing system.

The example computing device 602 as illustrated includes a processingsystem 604, one or more computer-readable media 606, and one or more I/Ointerface 608 that are communicatively coupled, one to another. Althoughnot shown, the computing device 602 may further include a system bus orother data and command transfer system that couples the variouscomponents, one to another. A system bus can include any one orcombination of different bus structures, such as a memory bus or memorycontroller, a peripheral bus, a universal serial bus, and/or a processoror local bus that utilizes any of a variety of bus architectures. Avariety of other examples are also contemplated, such as control anddata lines.

The processing system 604 is representative of functionality to performone or more operations using hardware. Accordingly, the processingsystem 604 is illustrated as including hardware element 610 that may beconfigured as processors, functional blocks, and so forth. This mayinclude implementation in hardware as an application specific integratedcircuit or other logic device formed using one or more semiconductors.The hardware elements 610 are not limited by the materials from whichthey are formed or the processing mechanisms employed therein. Forexample, processors may be comprised of semiconductor(s) and/ortransistors (e.g., electronic integrated circuits (ICs)). In such acontext, processor-executable instructions may beelectronically-executable instructions.

The computer-readable storage media 606 is illustrated as includingmemory/storage 612. The memory/storage 612 represents memory/storagecapacity associated with one or more computer-readable media. Thememory/storage component 612 may include volatile media (such as randomaccess memory (RAM)) and/or nonvolatile media (such as read only memory(ROM), Flash memory, optical disks, magnetic disks, and so forth). Thememory/storage component 612 may include fixed media (e.g., RAM, ROM, afixed hard drive, and so on) as well as removable media (e.g., Flashmemory, a removable hard drive, an optical disc, and so forth). Thecomputer-readable media 606 may be configured in a variety of other waysas further described below.

Input/output interface(s) 608 are representative of functionality toallow a user to enter commands and information to computing device 602,and also allow information to be presented to the user and/or othercomponents or devices using various input/output devices. Examples ofinput devices include a keyboard, a cursor control device (e.g., amouse), a microphone, a scanner, touch functionality (e.g., capacitiveor other sensors that are configured to detect physical touch), a camera(e.g., which may employ visible or non-visible wavelengths such asinfrared frequencies to recognize movement as gestures that do notinvolve touch), and so forth. Examples of output devices include adisplay device (e.g., a monitor or projector), speakers, a printer, anetwork card, tactile-response device, and so forth. Thus, the computingdevice 602 may be configured in a variety of ways as further describedbelow to support user interaction.

Various techniques may be described herein in the general context ofsoftware, hardware elements, or program modules. Generally, such modulesinclude routines, programs, objects, elements, components, datastructures, and so forth that perform particular tasks or implementparticular abstract data types. The terms “module,” “functionality,” and“component” as used herein generally represent software, firmware,hardware, or a combination thereof. The features of the techniquesdescribed herein are platform-independent, meaning that the techniquesmay be implemented on a variety of commercial computing platforms havinga variety of processors.

An implementation of the described modules and techniques may be storedon or transmitted across some form of computer-readable media. Thecomputer-readable media may include a variety of media that may beaccessed by the computing device 602. By way of example, and notlimitation, computer-readable media may include “computer-readablestorage media” and “computer-readable signal media.”

“Computer-readable storage media” may refer to media and/or devices thatenable persistent and/or non-transitory storage of information incontrast to mere signal transmission, carrier waves, or signals per se.Thus, computer-readable storage media refers to non-signal bearingmedia. The computer-readable storage media includes hardware such asvolatile and non-volatile, removable and non-removable media and/orstorage devices implemented in a method or technology suitable forstorage of information such as computer readable instructions, datastructures, program modules, logic elements/circuits, or other data.Examples of computer-readable storage media may include, but are notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical storage, harddisks, magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or other storage device, tangible media, orarticle of manufacture suitable to store the desired information andwhich may be accessed by a computer.

“Computer-readable signal media” may refer to a signal-bearing mediumthat is configured to transmit instructions to the hardware of thecomputing device 602, such as via a network. Signal media typically mayembody computer readable instructions, data structures, program modules,or other data in a modulated data signal, such as carrier waves, datasignals, or other transport mechanism. Signal media also include anyinformation delivery media. The term “modulated data signal” means asignal that has one or more of its characteristics set or changed insuch a manner as to encode information in the signal. By way of example,and not limitation, communication media include wired media such as awired network or direct-wired connection, and wireless media such asacoustic, RF, infrared, and other wireless media.

As previously described, hardware elements 610 and computer-readablemedia 606 are representative of modules, programmable device logicand/or fixed device logic implemented in a hardware form that may beemployed in some embodiments to implement at least some aspects of thetechniques described herein, such as to perform one or moreinstructions. Hardware may include components of an integrated circuitor on-chip system, an application-specific integrated circuit (ASIC), afield-programmable gate array (FPGA), a complex programmable logicdevice (CPLD), and other implementations in silicon or other hardware.In this context, hardware may operate as a processing device thatperforms program tasks defined by instructions and/or logic embodied bythe hardware as well as a hardware utilized to store instructions forexecution, e.g., the computer-readable storage media describedpreviously.

Combinations of the foregoing may also be employed to implement varioustechniques described herein. Accordingly, software, hardware, orexecutable modules may be implemented as one or more instructions and/orlogic embodied on some form of computer-readable storage media and/or byone or more hardware elements 610. The computing device 602 may beconfigured to implement particular instructions and/or functionscorresponding to the software and/or hardware modules. Accordingly,implementation of a module that is executable by the computing device602 as software may be achieved at least partially in hardware, e.g.,through use of computer-readable storage media and/or hardware elements610 of the processing system 604. The instructions and/or functions maybe executable/operable by one or more articles of manufacture (forexample, one or more computing devices 602 and/or processing systems604) to implement techniques, modules, and examples described herein.

The techniques described herein may be supported by variousconfigurations of the computing device 602 and are not limited to thespecific examples of the techniques described herein. This functionalitymay also be implemented all or in part through use of a distributedsystem, such as over a “cloud” 614 via a platform 616 as describedbelow.

The cloud 614 includes and/or is representative of a platform 616 forresources 618. The platform 616 abstracts underlying functionality ofhardware (e.g., servers) and software resources of the cloud 614. Theresources 618 may include applications and/or data that can be utilizedwhile computer processing is executed on servers that are remote fromthe computing device 602. Resources 618 can also include servicesprovided over the Internet and/or through a subscriber network, such asa cellular or Wi-Fi network.

The platform 616 may abstract resources and functions to connect thecomputing device 602 with other computing devices. The platform 616 mayalso serve to abstract scaling of resources to provide a correspondinglevel of scale to encountered demand for the resources 618 that areimplemented via the platform 616. Accordingly, in an interconnecteddevice embodiment, implementation of functionality described herein maybe distributed throughout the system 600. For example, the functionalitymay be implemented in part on the computing device 602 as well as viathe platform 616 that abstracts the functionality of the cloud 614.

CONCLUSION

Although the invention has been described in language specific tostructural features and/or methodological acts, it is to be understoodthat the invention defined in the appended claims is not necessarilylimited to the specific features or acts described. Rather, the specificfeatures and acts are disclosed as example forms of implementing theclaimed invention.

What is claimed is:
 1. In a digital medium environment to control outputof a natural language notification regarding a service provider, amethod implemented by at least one computing device, the methodcomprising: obtaining, by the at least one computing device, datadescribing metrics involving user interaction via a network with theservice provider; generating, by the at least one computing device,statements describing a plurality of different aspects of respectivesaid metrics and relationships of the metrics to each other; forming, bythe at least one computing device, at least one natural language phrasebased on the generated statements by rectifying a set of statementtemplates as including respective said statements; and outputting, bythe at least one computing device, the natural language notificationthat includes the at least one natural language phrase.
 2. The method asdescribed in claim 1, wherein the metrics describe at least onemetric-to-be-reported, in operation of the service provider or aninsight into a change observed in the user interaction.
 3. The method asdescribed in claim 1, wherein the generating of the statements is basedat least in part on types of tags assigned to respective said metrics.4. The method as described in claim 3, wherein the types of tagsinclude: semantic tags describing a type of user interaction with theservice provider; functional tags that describe a type of informationbeing tracked by a respective said metric; customer journey tagsdescribing a journey through digital content by a user in relation to arespective said metric; or persona tags describing a type of user thatis typically interested in a respective said metric.
 5. The method asdescribed in claim 1, wherein the data describes metrics involvingoperation of the service provider as part of making a product or serviceavailable for conversion via the network.
 6. The method as described inclaim 1, further comprising aggregating a plurality of said naturallanguage phrases to form the natural language notification.
 7. Themethod as described in claim 1, further comprising referencingcomplementary information in the natural language notification based atleast in part on the metrics included in the data.
 8. The method asdescribed in claim 1, further comprising arranging text, order, layout,or dispatch of the natural language notification based on at least oneuser preference.
 9. The method as described in claim 8, wherein the atleast one user preference is based on a user's role, profile, or usageinformation that is to receive the natural language notification. 10.The method as described in claim 1, wherein the data describes themetrics through use of a value of the metric and a timestamp and therelationships of the metrics to each other include a cause behind atleast one of the relationships based on the data over time.
 11. In adigital medium environment to control output of a natural languagenotification regarding a service provider, a method implemented by atleast one computing device, the method comprising: obtaining, by the atleast one computing device, data describing an alert involving acomparison of a metric of the service provider with a threshold;generating, by the at least one computing device, statements describingat least one other metric that has a likelihood of causing the alert ofthe metric; forming, by the at least one computing device, at least onenatural language phrase based on the generated statements by rectifyinga set of statement templates after inclusion of respective saidstatements; and outputting, by the at least one computing device, thenatural language notification that includes the at least one naturallanguage phrase.
 12. The method as described in claim 11, wherein thegenerating of the statements is based at least in part on types of tagsassigned to respective said metrics.
 13. The method as described inclaim 12, wherein the types of tags include: semantic tags describing atype of user interaction with the service provider; functional tags thatdescribe a type of information being tracked by a respective saidmetric; customer journey tags describing a journey through digitalcontent by a user in relation to a respective said metric; or personatags describing a type of user that is typically interested in arespective said metric.
 14. The method as described in claim 11, whereinthe generating of the statements is based at least in part on semanticanalysis that identifies a relationship between the metric and the atleast one other metric.
 15. The method as described in claim 11, whereinthe generating of the statements is based at least in part on comparisonof values of the at least one other metric to a threshold.
 16. In adigital medium environment to control output of a natural languagenotification regarding a service provider, a system comprising: a metriccollection module implemented at least partially in hardware to obtaindata describing metrics involving user interaction via a network withthe service provider; a statement generation module implemented at leastpartially in hardware to generate statements describing a plurality ofdifferent aspects of respective said metrics and relationships of themetrics to each other; a phrase generation module implemented at leastpartially in hardware to form at least one natural language phrase basedon the generated statements by rectifying a set of statement templatesafter inclusion of respective said statements.
 17. The system asdescribed in claim 16, wherein the metrics describe at least one anomalyin operation of the service provider.
 18. The system as described inclaim 16, wherein the data describes metrics involving operation of theservice provider as part of making a product or service available forconversion via the network.
 19. The system as described in claim 16,wherein the statement generation module is configured to generate thestatements based at least in part on types of tags assigned torespective said metrics.
 20. The system as described in claim 16,wherein the data describes the metrics through use of a value of themetric and a timestamp and the relationships of the metrics to eachother include a cause behind at least one of the relationships based onthe data over time.